Correlation Volume Pyramid.  In order to enhance the network capacity to learn detailed visual information, we further construct a correlation volume pyramid by incorporating multi-scale features.
Inspired by the work [1]}, we construct multi-scale representations by pooling over features, instead of input frames, to avoid the heavy computational overhead.
Specifically, we augment our network with an \(L\) -layer correlation pyramid \(\lbrace \mathbf {C}^k\rbrace _{k=1}^{L}\)  by max-pooling over the features of neighboring frames with \(L\)  kernels of growing sizes.
Thereby, the dimension of the correlation volume \(\mathbf {C}^{k}\)  is \(H^{\prime }\times W^{\prime }\times H^{\prime }/{2^k}\times W^{\prime }/{2^k}\) .
Different from [1]}, we downsample only the feature maps of the neighboring frame while keeping the resolution of the reference frame features.
As shown in [3]}, such design helps to model motions with multiple displacement scales while maintaining high-resolution image details.
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